Implications of Commons Act 2006 for the registration of land as a town or village green

This paper describes the main changes of Commons Act 2006 for the registration of land as a town or village green. The purpose of the Commons Act 2006 is to protect common land and promote sustainable farming, public access to the countryside and the interests of wildlife. The changes under s15 of the Commons Act 2006 include the additional 2-year grace period for application, discounting statutory period of closure, correction of mistakes in registers, disallowing severance of rights, voluntary registration, replacement of land in exchange and some other provisions. The transitional provision contained in s15(4) Commons Act 2006 is particularly a cause for controversy as DEFRA has indicated buildings will have to be taken down where development has gone ahead and a subsequent application to register the land as a green is successful, obliging the developer to return the land to a condition consistent with the exercise by locals of recreational rights, which sums up that it would be harder in future to develop land which has the potential to be registered as a town or village green.

Bayesian estimation of the spatial Durbin error model with an application to voter turnout in the 2004 presidential election

The potential for spatial dependence in models of voter turnout, although plausible from a theoretical perspective, has not been adequately addressed in the literature. Using recent advances in Bayesian computation, we formulate and estimate the previously unutilized spatial Durbin error model and apply this model to the question of whether spillovers and unobserved spatial dependence in voter turnout matters from an empirical perspective. Formal Bayesian model comparison techniques are employed to compare the normal linear model, the spatially lagged X model (SLX), the spatial Durbin model, and the spatial Durbin error model. The results overwhelmingly support the spatial Durbin error model as the appropriate empirical model.

Market transparency and collusion: on the UK agricultural tractor registration exchange

We review the decision by the European Commission in the case of the UK Agricultural Registration Exchange. We propose a theoretical model, offering a basis for some of the intuitive arguments used by the Commission on the anti-competitive role of information exchange in the case of price and non price collusion. Market transparency on non price data is shown to be a collusion facilitating device which may achieve stability in otherwise unstable cartels.

An ASIFT-based local registration method for satellite imagery

Imagery registration is a fundamental step, which greatly affects later processes in image mosaic, multi-spectral image fusion, digital surface modelling, etc., where the final solution needs blending of pixel information from more than one images. It is highly desired to find a way to identify registration regions among input stereo image pairs with high accuracy, particularly in remote sensing applications in which ground control points (GCPs) are not always available, such as in selecting a landing zone on an outer space planet. In this paper, a framework for localization in image registration is developed. It strengthened the local registration accuracy from two aspects: less reprojection error and better feature point distribution. Affine scale-invariant feature transform (ASIFT) was used for acquiring feature points and correspondences on the input images. Then, a homography matrix was estimated as the transformation model by an improved random sample consensus (IM-RANSAC) algorithm. In order to identify a registration region with a better spatial distribution of feature points, the Euclidean distance between the feature points is applied (named the S criterion). Finally, the parameters of the homography matrix were optimized by the Levenberg–Marquardt (LM) algorithm with selective feature points from the chosen registration region. In the experiment section, the Chang’E-2 satellite remote sensing imagery was used for evaluating the performance of the proposed method. The experiment result demonstrates that the proposed method can automatically locate a specific region with high registration accuracy between input images by achieving lower root mean square error (RMSE) and better distribution of feature points.